Machine and equipment assets are engineered to perform particular tasks as part of a process. For example, assets can include, among other things and without limitation, industrial manufacturing equipment on a production line, drilling equipment for use in mining operations, wind turbines that generate electricity on a wind farm, transportation vehicles, gas and oil refining equipment, and the like. As another example, assets may include devices that aid in diagnosing patients such as imaging devices (e.g., X-ray or MRI systems), monitoring equipment, and the like. The design and implementation of these assets often takes into account both the physics of the task at hand, as well as the environment in which such assets are configured to operate.
Low-level software and hardware-based controllers have long been used to drive machine and equipment assets. However, the rise of inexpensive cloud computing, increasing sensor capabilities, and decreasing sensor costs, as well as the proliferation of mobile technologies, have created opportunities for creating novel industrial and healthcare based assets with improved sensing technology and which are capable of transmitting data that can then be distributed throughout a network. As a consequence, there are new opportunities to enhance the business value of some assets through the use of novel industrial-focused hardware and software.
A production site or plant where goods are produced (e.g., manufacturing plant, factory, etc.) is an industrial site usually consisting of machines, equipment, raw materials, and the like, where workers operate machines to manufacture goods or to process one product into another. For example, industrial manufacturing plants may be used to produce food, beverages, electronics, paper, oil and gas, metals, appliances, and the like. A typical production site has multiple operational processes which occur on a regular and routine basis and which dictate how work orders are processed. For example, operational processes may include maintenance and repair of machinery and equipment, shift changes, downtime, power failures, scheduled events, and the like. However, these operational processes are difficult to analyze together because they do not involve the same variables and are therefore difficult to relate to one another. For example, it can be difficult to determine a contribution of particular operational process with respect to an overall delay or a shortage of production at the plant. Therefore, a technology is needed that is capable of bringing together different operating processes and analyzing the operating processes as a related group.